Biology subjects

Zang, Z.

Publications and source records attributed to Zang, Z..

16 recordsLinked to original sources

bioRxiv2026

NIR-II squeezed light-field microscopy enables high-speed volumetric imaging of deep-tissue dynamics in vivo

High-speed three-dimensional imaging in scattering biological tissues remains challenging because volumetric microscopy generally requires scanning, whereas snapshot light-field approaches divide limited detector pixels among multiple views. This constraint is particularly severe in the second near-infrared window (NIR-II), where InGaAs cameras have small sensor formats and high detector noise. Here we introduce NIR-II squeezed light-field microscopy (NIR-II SLIM), which optically rotates and compresses multiple perspective views before detection, allowing efficient use of camera pixels while retaining complementary spatial information for three-dimensional reconstruction. NIR-II SLIM acquires up to 600 volumes s-1 with a reconstructed lateral sampling grid of 512 x 512 pixels. We use this method for label-free imaging of cardiac dynamics in pigmented late-larval zebrafish, resolving chamber deformation and millisecond-scale atrioventricular-valve motion, and for NIR-II fluorescence imaging of vascular and lymphatic transport in mice. NIR-II SLIM provides a detector-efficient approach for high-speed volumetric imaging of rapid biological dynamics in scattering tissues.

bioengineering↗

bioRxiv2026

Balancing of immune activation and suppression during phage infection

Signaling-based anti-bacteriophage systems such as CBASS and Thoeris synthesize infection-triggered nucleotide signals that activate anti-phage effectors1,2. However, the phage features sensed by these systems and the mechanisms phages use to evade signaling immunity remain poorly understood. Here, studying clinically relevant Pseudomonas aeruginosa phages from the Migulavirinae family3, we show that closely related phages encode subtle allelic variation in side tail fiber proteins that determine sensitivity to type II Thoeris. In parallel, these same phages encode an "anti-defense hotspot" that contains three adjacent genes that are each sufficient to facilitate phage evasion of both CBASS and Thoeris defenses, counter-balancing the activating proteins. Comparative analysis of this anti-signaling hotspot across the broader family of related N4-like phages uncovered a new Thoeris anti-defense (Tad) protein that sponges NAD-derived molecules (e.g. gcADPR) and exhibits sequence and structural similarity to a poorly characterized nucleotide-binding region of the human ryanodine receptor. Together, these findings reveal how the balance between immune activation and antagonism shifts phage outcomes and reveals a surprising similarity between a phage molecular sponge and an important human protein.

microbiology↗

bioRxiv2026

Chemical suppression of a bacterial immune system revives repressed phages

Many antiviral immune systems have recently been discovered in bacteria. The mechanisms of several are obscure, as are their individual significance for antiphage defense. To shed light on the mechanism and significance of the two-component type I Thoeris antiphage immune system, we leveraged high-throughput phenotypic screening to identify three small molecule inhibitors. The inhibitors target the ThsA NADase component, inhibiting its 3'-cADPR-activated filamentation. The temporal control afforded by the small-molecule inhibitors allowed us to answer an outstanding question in antiviral immunity--is persistent immunity required to repress phage titers, or do immune systems become unnecessary after eradicating infectious phages? We found that Thoeris immunity must be maintained, as chemical inhibition enabled repressed phages to revive and overtake the bacterial population. Furthermore, due to the cooperative nature of antiviral immunity, we found that Thoeris must be inhibited in only 10% of the bacteria to cause phage-induced lysis of the entire population.

biochemistry↗

bioRxiv2026

CellBench-LS: Benchmark Evaluation of Single-cell Foundation Models for Low-supervision Scenarios

Single-cell foundation models are trained on millions of cells, but their value when downstream labels are scarce remains uncertain. Here we compare seven such models with three established representations across clustering, batch correction, cell type annotation, gene expression reconstruction and perturbation prediction. Frozen representations are tested directly for clustering and batch correction and with lightweight task heads for few-shot prediction. CellPLM led the aggregate clustering, annotation and batch-correction comparisons, scMulan led perturbation prediction, and principal component analysis led reconstruction. Of the seven foundation models, one exceeded the strongest classical baseline in clustering, three in batch correction, four in annotation, none in reconstruction and six in perturbation prediction. Representation analyses linked reconstruction performance to preservation of linear gene relationships, but cross-model gene-relation agreement did not exceed a dimension-matched random-projection null. These results show that the utility of a cell representation depends on the information required by the downstream task.

cell biology↗

bioRxiv2026

Filament formation is a conserved mechanism of Thoeris SIR2 effector activation

Thoeris defence systems protect bacteria from phages via abortive infection. In type I Thoeris systems, ThsA effectors containing silent information regulator 2 (SIR2) and SMF/DprA-LOG (SLOG) domains are activated by the cyclic ADP-ribose (ADPR) isomer 3'cADPR, triggering abortive infection via nicotinamide adenine dinucleotide (NAD+) depletion. 3'cADPR activates the NADase activity of Bacillus subtilis ThsA tetramers via filament formation of its SIR2 domains, but the molecular details of how 3'cADPR triggers this process remain incompletely understood. Here, we demonstrate that ThsA activation by 3'cADPR-induced SIR2 filament formation is conserved in type I Thoeris systems from Streptococcus equi and Entercoccus facium. We present cryo-electron microscopy structures of the S. equi ThsA filament bound to 3'cADPR and the non-cleavable NAD+ analog carba-NAD+, and of the S. equi ThsA tetramer bound to 3'cADPR. These structures reveal that SIR2 filament formation is required to stabilise an active site conformation that can bind and hydrolyse NAD+. The structures also show that 3'cADPR induces quaternary alterations in the SLOG dimers and consequently the SIR2 tetramer to enable ThsA filament formation. Collectively, our study provides a comprehensive understanding of 3'cADPR-induced activation of type I Thoeris effectors.

microbiology↗

bioRxiv2025

Expanding the RNA Virus Universe by Scalable Structure-Guided Discovery

The discovery of RNA viruses from metatranscriptomic data remains challenging due to their extreme sequence divergence and frequent lack of conserved motifs. We present Rider, a lightweight two-stage framework that couples fast, structure-informed sequence screening with targeted structural validation. Stage 1 uses a compact 35M-parameter protein language model to prioritize RdRp-like fragments at whole-sample scale, achieving over 44x higher end-to-end screening throughput on commodity hardware. Stage 2 applies structure prediction and Foldseek-based alignment against a dedicated RdRp structure resource ([~]200k ESMFold-predicted structures), providing orthogonal evidence for remote homologs. Applied to >10,000 metatranscriptomes spanning marine, freshwater, soil and host-associated microbiomes, Rider matches or outperforms leading tools (e.g., LucaProt, PalmScan) and additionally recovers divergent and truncated sequences. Multiple orthogonal indicators, including structure consistency and low DNA read mapping to corresponding contigs, support genuine RNA origin. In a human IBD cohort, Rider agrees with state-of-the-art calls for clinically relevant RNA viruses while extending discovery to divergent lineages. Rider turns structure-guided homology search into a practical, scalable pipeline for RNA virome discovery. HighlightO_LIA two-stage framework enables structure-guided RNA virus discovery at sample scale, achieving up to 44-fold higher throughput on standard computing hardware. C_LIO_LIThe method matches or surpasses LucaProt and PalmScan across >10,000 metatranscriptomes from diverse environments, while recovering RdRp fragments missed by existing tools. C_LIO_LIStructural validation using [~]200,000 ESMFold-predicted RdRp models and Foldseek alignment supports the detection of remote homologs with high confidence. C_LIO_LIOrthogonal evidence, including low DNA read mapping, strand-specific expression, and ORF metrics, confirms RNA origin and reduces false positives.. C_LIO_LIOpen-source code and an openly released RdRp structure database enable scalable, reproducible RNA virome discovery in environmental and clinical settings. C_LI

bioinformatics↗

bioRxiv2025

Spatially variable growth responses to warming in Atlantic Sea Scallops (Placopecten magellanicus)

Atlantic sea scallop is an important commercial species in the U.S., and its growth rate is strongly modulated by temperature. In this study, we quantified the interannual variations in scallop growth rate and thermal stress intensity in the Mid-Atlantic Bight (MAB) from 2000 to 2018. The results showed that the scallop growth rate variations were overall synchronized between the shallow (< 60 m) and deep ([≥] 60 m) MAB prior to 2015. The response of growth rate to warming in 2015 and 2016 showed marked spatial heterogeneity: scallops in the shallow subregions grew more slowly in the warm years, whereas higher temperature contributed to elevated growth rates in the deep subregions. We developed a dynamic energy budget model to explain the distinct growth rate response to warming at different depths. The model results showed that, in 2016, bottom temperatures in the shallow subregions exceeded the optimal range, limiting the energy available for growth. In contrast, warming created more favorable thermal conditions in the deep habitats. This work reveals the spatiotemporal patterns of scallop growth rates in the MAB and contributes a quantitative understanding of thermal stress and the development of science-based management strategies under future warming.

ecology↗

bioRxiv2025

Chemical inhibition of a bacterial immune system

The rise of antibiotic resistance motivates a revived interest in phage therapy. However, bacteria possess dozens of anti-bacteriophage immune systems that confer resistance to therapeutic phages. Chemical inhibitors of these anti-phage immune systems could be employed as adjuvants to overcome resistance in phage-based therapies. Here, we report that anti-phage systems can be selectively inhibited by small molecules, thereby sensitizing phage-resistant bacteria to phages. We discovered a class of chemical inhibitors that inhibit the type II Thoeris anti-phage immune system. These inhibitors block the biosynthesis of a histidine-ADPR intracellular alarm signal by ThsB and prevent ThsA from arresting phage replication. These inhibitors promiscuously inhibit type II Thoeris systems from diverse bacteria--including antibiotic-resistant pathogens. Chemical inhibition of the Thoeris defense improved the efficacy of a model phage therapy against a phage-resistant strain of P. aeruginosa in a mouse infection, suggesting a therapeutic potential. Furthermore, these inhibitors may be employed as chemical tools to dissect the importance of the Thoeris system for phage defense in natural microbial communities.

biochemistry↗

bioRxiv2024

Bacterium secretes chemical inhibitor that sensitizes competitor to bacteriophage infection

To overtake competitors, microbes produce and secrete secondary metabolites that kill neighboring cells and sequester nutrients. This natural product-mediated competition likely evolved in complex microbial communities that included viral pathogens. From this ecological context, we hypothesized that microbes secrete metabolites that "weaponize" natural pathogens (i.e., bacteriophages) to lyse their competitors. Indeed, we discovered a bacterial secondary metabolite that sensitizes other bacteria to phage infection. We found that this metabolite provides the producer (a Streptomyces sp.) with a fitness advantage over its competitor (Bacillus subtilis) by promoting phage infection. The phage-promoting metabolite, coelichelin, sensitized B. subtilis to a wide panel of lytic phages, and it did so by preventing the early stages of sporulation through iron sequestration. Beyond coelichelin, other natural products may provide phage-mediated competitive advantages to their producers--either by inhibiting sporulation or through yet-unknown mechanisms.

microbiology↗

bioRxiv2023

Failed reprogramming of transformed cells due to induction of apoptosis and senescence impairs tumor progression in lung cancer

Cell reprogramming to pluripotency applied to the study of cancer has identified transformation and pluripotency as two independent and incompatible cell fates. A detailed knowledge of the relationship between transformation and reprogramming could lead to the identification of new vulnerabilities and therapeutic targets in cancer. Here, we explore this interplay and find that OSKM expression limits tumor cell growth by inducing apoptosis and senescence. We identify Oct4 and Klf4 as the main individual reprogramming factors responsible for this effect. Mechanistically, the induction of cell cycle inhibitor p21 downstream of the reprogramming factors acts as mediator of cell death and senescence. Using a variety of in vivo systems, including allografts, orthotopic transplantation and KRAS-driven lung cancer mouse models, we demonstrate that OSKM expression impairs tumor growth and reduces tumor burden.

cell biology↗

bioRxiv2022

Structure-Preserving and Batch-Correcting Visualization Using Deep Manifold Transformation for Single-cell RNA-Seq Profiles

Dimensionality reduction and visualization play an important role in biological data analysis, such as data interpretation of single-cell RNA sequence (scRNA-seq). It is desired to have a visualization method that can not only be applicable to various application scenarios, including cell clustering and trajectory inference, but also satisfy a variety of technical requirements, especially the ability to preserve inherent structure of data and handle with batch effects. However, no existing methods can accommodate these requirements in a unified framework. In this paper, we propose a general visualization method, deep visualization (DV), that possesses the ability to preserve inherent structure of data and handle batch effects and is applicable to a variety of datasets from different application domains and dataset scales. The method embeds a given dataset into a 2- or 3-dimensional visualization space, with either a Euclidean or hyperbolic metric depending on a specified task type with type static (at a time point) or dynamic (at a sequence of time points) scRNA-seq data, respectively. Specifically, DV learns a structure graph to describe the relationships between data samples, transforms the data into visualization space while preserving the geometric structure of the data and correcting batch effects in an end-to-end manner. The experimental results on nine datasets in complex tissue from human patients or animal development demonstrate the competitiveness of DV in discovering complex cellular relations, uncovering temporal trajectories, and addressing complex batch factors. We also provide a preliminary attempt to pre-train a DV model for visualization of new incoming data.

bioinformatics↗

bioRxiv2021

A unified model of amino acid homeostasis in mammalian cells

Homeostasis is one of the fundamental concepts in physiology. Despite remarkable progress in our molecular understanding of amino acid transport, metabolism and signalling, it remains unclear by what mechanisms cytosolic amino acid concentrations are maintained. We propose that amino acid transporters are the primary determinants of intracellular amino acid levels. We show that a cells endowment with amino acid transporters can be deconvoluted by a logical series of experiments. This was used to computationally simulate amino acid translocation across the plasma membrane. For two different cancer cell lines and human myotubes, transport simulation generates cytosolic amino acid concentrations that are close to those observed in vitro. Perturbations of the system were replicated in silico and could be applied to systems where only transcriptomic data are available. The methodology developed in this study is widely applicable to other transport processes and explain amino acid homeostasis at the systems-level.

systems biology↗

bioRxiv2020

Phenotype Prediction using a Tensor Representation and Deep Learning from Data Independent Acquisition Mass Spectrometry

A novel approach for phenotype prediction is developed for mass spectrometric data. First, the data-independent acquisition (DIA) mass spectrometric data is converted into a novel file format called "DIA tensor" (DIAT) which contains all the peptide precursors and fragments information and can be used for convenient DIA visualization. The DIAT format is fed directly into a deep neural network to predict phenotypes without the need to identify peptides or proteins. We applied this strategy to a collection of 102 hepatocellular carcinoma samples and achieved an accuracy of 96.8% in classifying malignant from benign samples. We further applied refined model to 492 samples of thyroid nodules to predict thyroid cancer; and achieved a predictive accuracy of 91.7% in an independent cohort of 216 test samples. In conclusion, DIA tensor enables facile 2D visualization of DIA proteomics data as well as being a new approach for phenotype prediction directly from DIA-MS data.

bioinformatics↗

bioRxiv2019

Brain state stability during working memory is explained by network control theory, modulated by dopamine D1/D2 receptor function, and diminished in schizophrenia

Dynamical brain state transitions are critical for flexible working memory but the network mechanisms are incompletely understood. Here, we show that working memory entails brain-wide switching between activity states. The stability of states relates to dopamine D1 receptor gene expression while state transitions are influenced by D2 receptor expression and pharmacological modulation. Schizophrenia patients show altered network control properties, including a more diverse energy landscape and decreased stability of working memory representations.

neuroscience↗

bioRxiv2019

Generative network models identify biological mechanisms of altered structural brain connectivity in schizophrenia

BackgroundAlterations in the structural connectome of schizophrenia patients have been widely characterized, but the mechanisms leading to those alterations remain largely unknown. Generative network models have recently been introduced as a tool to test the biological underpinnings of the formation of altered structural brain networks.\n\nMethodsWe evaluated different generative network models to investigate the formation of structural brain networks in healthy controls (n=152), schizophrenia patients (n=66) and their unaffected first-degree relatives (n=32), and we identified spatial and topological factors contributing to network formation. We further investigated the association of these factors to cognition and to polygenic risk for schizophrenia.\n\nResultsStructural brain networks can be best accounted for by a two-factor model combining spatial constraints and topological neighborhood structure. The same wiring model explained brain network formation for all groups analyzed. However, relatives and schizophrenia patients exhibited significantly lower spatial constraints and lower topological facilitation compared to healthy controls. The model parameter for spatial constraint was correlated with the polygenic risk for schizophrenia and predicted reduced cognitive performance.\n\nConclusionsOur results identify spatial constraints and local topological structure as two interrelated mechanisms contributing to normal brain development as well as altered connectomes in schizophrenia. Spatial constraints were linked to the genetic risk for schizophrenia and general cognitive functioning, thereby providing insights into their biological basis and behavioral relevance.

neuroscience↗

bioRxiv2019

Neural network-based alterations during repeated heat pain stimulation in major depression

The current study aimed to identify alterations in brain activation and connectivity related to nociceptive processing and pain sensitization in major depressive disorder (MDD), using repeated heat pain stimulation during functional magnetic resonance imaging (fMRI) in 37 MDD patients and 33 healthy controls. Regional activation did not differ between groups, but functional connectivity was significantly decreased in MDD in a neural network connecting frontal, temporal and occipital areas (family-wise error (FWE)-corrected pFWE = 0.045). Supplemental analyses suggested a significant association between network connectivity and trait neuroticism (p = 0.007) but not with the clinical state or familiar risk of MDD (all p values > 0.13). Our data relate a network-based phenotype for altered pain processing and antinociceptive control to MDD and encourage future studies on the shared intermediate neural psychological risk architecture of MDD and chronic pain.

neuroscience↗